Abstract PO2-07-04: Applying the Alliance Trial Guidelines in Multi-focal Breast Disease Using an Artificial Intelligence Computational Platform: Economic Analysis and Cosmetic Sensitivity

John Pfeiffer,Matthew Biancalana,Dorys Lopez-Ramos, Bradley Feiger, Anuja Antony

Cancer Research(2024)

引用 0|浏览0
暂无评分
摘要
Abstract Background: Traditionally, multi-focal breast cancer results in mastectomy. The Alliance Trial offers a paradigm shift in surgical options available for multi-focal breast cancer patients in the context of adjuvant chemotherapy. In the trial, patients with multi-focal disease (< 3 tumors) who underwent breast conservation surgery (BCS) were found to have similar outcomes to patients undergoing mastectomy. BCS for large volume tumors ( >30%) has been cited as having a high potential for cosmetic defect, and hence represents a typical upper limit for potential tissue removal in BCS. Here, we evaluated a patient cohort to better understand the economic impact of the Alliance trial and further categorize patients that would most benefit without suffering cosmetic impact. We employed a novel computational technology to quantify the ratio of tumor size to breast tissue volume. Methods: Using a publicly available, single site cohort (n=243, DUMC) of breast cancer patients that underwent mastectomy, we segmented the tumors using our TumorSight Viz software platform. This platform uses artificial intelligence to segment the tumor and surrounding tissues and allows for a volumetric and morphologic assessment in 3D space. We then applied relevant inclusion/exclusion criteria from the Alliance Trial to the cohort (Saha et al, 2018). In trial eligible patients, we used TumorSight Viz to create a convex hull (CH) around the multi-focal disease using dilations of 1 cm and 2 cm. The volume of the CH, corresponding to proposed surgical extirpation, and the overall breast volume (BV) were then computationally assessed in 3D. The ratio of CH to BV (CH:BV) was calculated and a cutoff of 30% (high potential for cosmetic deformity) was applied. A cost analysis was then carried out. We determined the aggregate per annum savings that could potentially be realized by transforming a subset of mastectomies to BCS by tabulating total costs of mastectomy+reconstruction vs. BCS+WBI (whole-breast irradiation), as well as adjusted for relative rates of adjuvant therapy (~80%) across the nationwide patient population. Results: We found that 19.3% of adjuvant mastectomy patients were eligible for BCS based on Alliance Trial criteria. Of those, 68% had tumor CH:BV < 30% when using a 1 cm dilation around the tumor. When using a 2 cm dilation, 56% had tumor CH:BV < 30%. Together, these results indicate that of all adjuvant mastectomy patients, an estimated 10.8-13.1% are eligible for BCS based on volumetric measures of cosmetically acceptable breast tissue removal. Our economic analysis of BCS vs. mastectomy revealed an estimated $28,500 cost savings for patients with private insurance, suggesting that both decreased costs and improved quality-of-life (QOL) can be mutually aligned. By assessing the nationwide number of patients receiving adjuvant therapy for breast cancer, alongside the percentage potentially eligible for BCS using the above cosmetic defect analysis, we estimate that BCS conversion from mastectomy offers to provide a net savings of $300-350 million annually. Conclusion: The Alliance Trial guidelines unveiled the potential option of BCS in ~20% of patients with multi-focal disease in our cohort, demonstrating considerable cost-savings. Computational tools can further differentiate individuals who may not be best candidates for BCS in this setting, ensuring high QOL and informed decision-making. Citation Format: John Pfeiffer, Matthew Biancalana, Dorys Lopez-Ramos, Bradley Feiger, Anuja Antony. Applying the Alliance Trial Guidelines in Multi-focal Breast Disease Using an Artificial Intelligence Computational Platform: Economic Analysis and Cosmetic Sensitivity [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-04.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要